Runout Number: A New Metric for Landslide Runout Characterization

ABSTRACT Landslide runout has traditionally been quantified by the height-to-length ratio, H/L, which, in many cases, is strongly influenced by the slope of the runout path. In this study, we propose an alternative mobility measure, the unitless Runout Number, measured as the landslide length divided by the square root of the landslide area, which characterizes landslide shape in terms of elongation. We used a database of 158 landslides of varying runout distances from locations in northern California, Oregon, and Washington state to compare the two runout measurement methods and explore their predictability using parameters that can be measured or estimated using geographic information systems. The Runout Number better describes the overall runout for several landslide and slope geometries. The two mobility measures show very little correlation to each other, indicating that the two parameters describe different landslide mobility mechanisms. When compared to predictive parameters shown by prior research to relate to landslide runout, the two runout measurement methods show different correlations. H/L correlates more strongly to initial slope angle, upslope contributing area, landslide area, and grain size distribution (percent clay, silt, total fines, and sand). The Runout Number correlates more strongly to planimetric curvature, upslope contributing area normalized by landslide area, and percent sand. Although these correlations are not necessarily strong enough for prediction, they indicate the validity of both runout measurement methods and the benefit of including both numbers when characterizing landslide mobility.

DebrisFlow Predictor: an agent-based runout program for shallow landslides

Credible models of landslide runout are a critical component of hazard and risk analysis in the mountainous regions worldwide. Hazard analysis benefits enormously from the number of available landslide runout models that can recreate events and provide key insights into the nature of landsliding phenomena. Regional models that are easily employed, however, remain a rarity. For debris flows and debris avalanches, where the impacts may occur some distance from the source, there remains a need for a practical, predictive model that can be applied at the regional scale. We present, herein, an agent-based simulation for debris flows and debris avalanches called DebrisFlow Predictor. A fully predictive model, DebrisFlow Predictor employs autonomous subroutines, or agents, that act on an underlying digital elevation model (DEM) using a set of probabilistic rules for scour, deposition, path selection, and spreading behavior. Relying on observations of aggregate debris flow behavior, DebrisFlow Predictor predicts landslide runout, area, volume, and depth along the landslide path. The results can be analyzed within the program or exported in a variety of useful formats for further analysis. A key feature of DebrisFlow Predictor is that it requires minimal input data, relying primarily on a 5 m DEM and user-defined initiation zones, and yet appears to produce realistic results. We demonstrate the applicability of DebrisFlow Predictor using two very different case studies from distinct geologic, geomorphic, and climatic settings. The first case study considers sediment production from the steep slopes of Papua, the island province of Indonesia; the second considers landslide runout as it affects a community on Vancouver Island off the west coast of Canada. We show how DebrisFlow Predictor works, how it performs compared to real world examples, what kinds of problems it can solve, and how the outputs compare to historical studies. Finally, we discuss its limitations and its intended use as a predictive regional landslide runout tool. DebrisFlow Predictor is freely available for non-commercial use.

A combined procedure to assess rainfall-induced shallow landslide detachment, transit and runout susceptibility using Machine Learning and GIS techniques

<p>Rainfall-induced landslides are notoriously dangerous phenomena which can cause a notable death toll as well as major economic losses globally. Usually, shallow landslides are triggered by prolonged or severe rainfalls and frequently may evolve into potentially catastrophic flow-like movements. Shallow failures are typical in hilly and mountainous areas due to the combination of several predisposing factors such as slope morphology, geological and structural setting, mechanical properties of soils, hydrological and hydrogeological conditions, land-use changes and wildfires. Because of the ability of these phenomena to travel long distances, buildings and infrastructures located in areas improperly deemed safe can be affected.</p><p>Spatial and temporal hazard posed by flow-like movements is due to both source characteristics (e.g., location and volume) and the successive runout dynamics (e.g., travelled paths and distances). Hence, the assessment of shallow landslide susceptibility has to take into account not only the recognition of the most probable landslide source areas, but also  landslide runout (i.e., travel distance). In recent years, a meaningful improvement in landslide detachment susceptibility evaluation has been gained through robust scientific advances, especially by using statistical approaches. Furthermore, various techniques are available for landslide runout susceptibility assessment in quantitative terms. The combination of landslide detachment and runout dynamics has been admitted by many researchers as a suitable and complete procedure for landslide susceptibility evaluation. However, despite its significance, runout assessment is not as widespread in literature as landslide detachment assessment and still remains a challenge for researchers. Currently, only a few studies focus on the assement of both landslide detachment susceptibility (LDS) and landslide runout susceptibility (LRS).</p><p>In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. Such procedure is based on the integration between LDS assessment via Machine Learning techniques (applying the Ensemble approach) and LRS assessment through GIS-based tools (using the “reach angle” method). This methodology has been applied to the Cinque Terre National Park (Liguria, north-west Italy), where risk posed by flow-like movements is very high. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. In particular, the obtained map may be useful for urban and regional planning, as well as for decision-makers and stakeholders, to predict areas that may be affected by rainfall-induced shallow landslides  in the future and to identify areas where risk mitigation measures are needed.</p>

A landslide granular phase transition

Landslides appear in many different forms and sizes, from devastating debris flows to overly optimistic sand castles. A familiar and well-documented feature of all landslides is the positive correlation between volume and landslide runout. Larger landslides have a greater potential to reach farther. Here we explore the low volume limit and find a surprising negative correlation between volume and landslide runout. A decrease in volume leads to an increase in runout. In a series of experiments we systematically vary the landslide volume to reveal a transition between these two regimes.

Landslide hazard assessment by smoothed particle hydrodynamics with spatially variable soil properties and statistical rainfall distribution

Rainfall-induced landslides have caused significant damage to structures and casualties in the past decades, and it is of great importance to assess the post-failure behavior of slopes. This study proposes a probabilistic framework to evaluate the hazards associated with landslide runout arising from loose-fill slope failures. The failure process is simulated by the smoothed particle hydrodynamics (SPH) method, which is capable of capturing large deformations of landslides. The shear strength parameters of the soils are modeled as random variables, and random field simulations are performed to explore the effects of soil variability on the runout distance. In addition, the uncertainty in rainfall characteristics is represented by the Gumbel distribution, with the ensuing rainfall infiltration simulated in multiple seepage analyses to obtain pore pressure profiles in the slope, which are then adopted as initial conditions for the SPH method. Combining these various sources of uncertainty, the hazard factors indicating the risks for nearby structures are quantified based on the response uncertainty in landslide runout distances. To demonstrate this framework, the hazard levels associated with two typical layouts of loose-fill slopes are evaluated, and the results may serve as risk zoning indicators for adjacent developments.

LABS: an agent-based run-out program for shallow landslides

Credible models of landslide runout are a critical component of hazard and risk analysis in the mountainous regions worldwide. Hazard analysis benefits enormously from the number of available landslide runout models that can recreate events and provide key insights into the nature of landsliding phenomena. Regional models that are easily employed, however, remain a rarity. For debris flows and debris avalanches, where the impacts may occur some distance from the source, there remains a need for a practical, predictive model that can be applied at the regional scale. We present, herein, an agent-based simulation for debris flows and debris avalanches called LABS. A fully predictive model, LABS employs autonomous sub-routines, or agents, that act on an underlying DEM using a set of probabilistic rules for scour, deposition, path selection, and spreading behavior. Relying on observations of aggregate debris flow behavior, LABS predicts landslide runout, area, volume, and depth along the landslide path. The results can be analyzed within the program or exported in a variety of useful formats for further analysis. A key feature of LABS is that it requires minimal input data, relying primarily on a 5 m DEM and user defined initiation zones, and yet appears to produce realistic results. We demonstrate the applicability of LABS using two very different case studies from distinct geologic, geomorphic, and climatic settings. The first case study considers sediment production from the steep slopes of Papua, the island province of Indonesia; the second considers landslide runout as it affects a community on Vancouver Island off the west coast of Canada. We show how LABS works, how it performs compared to real world examples, what kinds of problems it can solve, and how the outputs compare to historical studies. Finally, we discuss its limitations and its intended use as a predictive regional landslide runout tool. LABS is freely available to not for profit groups including universities, NGOs and government organizations.